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.github/workflows/python-app.yml

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# This workflow will install Python dependencies, run tests and lint with a single version of Python
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# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
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name: Python application
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name: CI Tests
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push:
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- name: Lint with flake8
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run: |
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# stop the build if there are Python syntax errors or undefined names
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flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
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flake8 . --ignore=E501,F401,W503,E266 --count --select=E9,F63,F7,F82 --show-source --statistics
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# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
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flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
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flake8 . --ignore=E501,F401,W503,E266 --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
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- name: Test with unittest
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run: |
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python -m unittest discover src/test_pownet

README.md

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![license MIT](https://img.shields.io/github/license/kamal0013/PowNet)
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![license MIT](https://img.shields.io/badge/License-MIT-yellow.svg)
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[![CI Tests](https://github.com/Critical-Infrastructure-Systems-Lab/PowNet/actions/workflows/python-app.yml/badge.svg)](https://github.com/Critical-Infrastructure-Systems-Lab/PowNet/actions/workflows/python-app.yml)
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# PowNet: Unit Commitment / Economic Dispatch model in Python
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PowNet is a least-cost optimization model for simulating the Unit Commitment and Economic Dispatch (UC/ED) of large-scale (regional to country) power systems. In PowNet, a power system is represented by a set of nodes that include power plants, high-voltage substations, and import/export stations (for cross-border systems). The model schedules and dispatches the electricity supply from power plant units to meet hourly electricity demand in substations at a minimum cost. It considers the techno-economic constraints of both generating units and high-voltage transmission network. The power flow calculation is based on a Direct Current (DC) network (with N-1 criterion), which provides a reasonable balance between modelling accuracy and data and computational requirements. PowNet can easily integrate information about variable renewable resources (e.g., hydro, wind, solar) into the UC/ED process. For example, it can be linked with models that estimate the electricity supply available from renewable resources as a function of the climatic conditions. In addition, PowNet has provision to account for the effect of droughts on the generation of dispatchable thermal units (e.g., coal, oil, gas-fired units) that depend on freshwater availability. These features facilitate the application of PowNet to problems in the water-energy nexus domain that investigate the impact of water availability on electricity supply and demand.
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